Particulate Matter Induces Cytokine Expression in Human Bronchial Epithelial Cells
Bibliographic record
Abstract
The present study was designed to determine cytokines produced by primary human bronchial epithelial cells (HBECs) exposed to ambient air pollution particles (EHC-93). Cytokine messenger RNA (mRNA) was measured using a ribonuclease protection assay and cytokine protein production by enzyme-linked immunosorbent assay. Primary HBECs were freshly isolated from operated lung, cultured to confluence, and exposed to 10 to 500 microg/ml of a suspension of ambient particulate matter with a diameter of less than 10 microm (PM(10)) for 2, 8, and 24 h. The mRNA levels of leukemia inhibitory factor (LIF), granulocyte macrophage colony-stimulating factor (GM-CSF), interleukin (IL)-1alpha, and IL-8 were increased after exposure to PM(10), and this increase was dose-dependent between 100 (P < 0.05) and 500 (P < 0.05) microg/ml of PM(10) exposure. The concentrations of LIF, GM-CSF, IL-1beta, and IL-8 protein measured in the supernatant collected at 24 h increased in a dose- dependent manner and were significantly higher than those in the control nonexposed cells. The soluble fraction of the PM(10) (100 microg/ml) did not increase these cytokine mRNA levels compared with control values and were significantly lower compared with HBECs exposed to 100 microg/ml of PM(10) (LIF, IL-8, and IL-1beta; P < 0.05), except for GM-CSF mRNA (P = not significant). We conclude that primary HBECs exposed to ambient PM(10) produce proinflammatory mediators that contribute to the local and systemic inflammatory response, and we speculate that these mediators may have a role in the pathogenesis of cardiopulmonary disease associated with particulate air pollution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".